2026-09-23T07:01:55Z https://www.ideals.illinois.edu/oai-pmh

oai:www.ideals.illinois.edu:2142/108634 2023-07-11 col_2142_5131 col_2142_10761 com_2142_5130 com_2142_10755 com_2142_234

Bailey, Brian P Schoening, Mia Johanna 2020-10-07T22:44:42Z 2020-10-07T22:44:42Z 2022-10-07T22:44:53Z 2020-07-21 2020-08 Online content annotation tools have become increasingly popular as more and more content is being presented in a digital, written format. Yet such tools are still needed in the domain of creative feedback, where annotation is critical in helping users to effectively interpret feedback and make quality revisions. We therefore present a novel content annotation tool (CATIA) aimed at supporting users in annotating collections of written feedback through categorization. Because the process of categorizing feedback is one that is often tedious and time-consuming, our tool integrates intelligent assistance in the form of recommendations, to further assist people as they annotate feedback documents. Through a small study (N = 4) evaluating CATIA against a baseline tool without intelligent assistance, we found that all participants preferred the use of CATIA to complete feedback categorization tasks. The recommendations were shown to be effective at clustering similar statements within a feedback document, thereby reducing the frequency with which users must switch focus between categories. Participants thus responded positively to the integration of the intelligent assistance, crediting the recommendations with making the annotation process easier and less overwhelming overall. Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2022-08-01 The student, Mia Schoening, accepted the attached license on 2020-07-21 at 11:39. The student, Mia Schoening, submitted this Thesis for approval on 2020-07-21 at 11:57. This Thesis was approved for publication on 2020-07-21 at 13:19. DSpace SAF Submission Ingestion Package generated from Vireo submission #15710 on 2020-10-02 at 15:34:00 Made available in DSpace on 2020-10-07T22:44:42Z (GMT). No. of bitstreams: 2 SCHOENING-THESIS-2020.pdf: 3446644 bytes, checksum: 313771b6803029fbdf15fea96e919802 (MD5) LICENSE.txt: 4210 bytes, checksum: ad83ad9cd91a0183317d4df493cfcc1f (MD5) Previous issue date: 2020-07-21 Embargo set by: Seth Robbins for item 116261 Lift date: 2022-10-07T22:44:53Z Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD system Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD system U of I Only application/pdf http://hdl.handle.net/2142/108634 en Copyright 2020 Mia Schoening Content Annotation Feedback Intelligent Assistance An intelligent tool for annotating collections of written feedback text Thesis Computer Science Computer Science University of Illinois at Urbana-Champaign Thesis M.S.